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I've always wanted to properly understand the data around me — but having a dataset was never the same as having answers. At an EV charging startup, where I worked on the app and management platform for our chargepoints, I spent a lot of time digging through chargepoint and user activity data. We wanted to develop an analytics feature so chargepoint operators could easily dig into their business and see where to improve — but it was completely scrapped, as we didn't have the resources for it. We lost a critical value proposition in that startup purely because of this barrier around large-scale data analysis.

That frustrated me, because I'm a developer — I could code my way to an answer, eventually. It made me realize how much worse this is for everyone who can't: a lot of businesses have valuable data just sitting there, unused, because using it requires resources they don't have. When AI matured, I thought it could finally close that gap — but the tools weren't built for it. Most LLMs choke on real-world file sizes, can't connect to a CRM or database directly, and being U.S.-based makes trusting them with your business data a hard sell.

I have a background in Business Economics and IT, and I'm a self-taught developer — I also work in QA today, which is probably why I care so much about getting things right, both in what the system finds and whether it's actually relevant to you. DataDigger is my attempt to close that gap: privacy-first, trustworthy, and built to democratize data analysis — so that having a dataset is actually enough to get value out of it, whether or not you can code. I'm building this together with a colleague from that same startup. The goal, ultimately, is simple: data shouldn't sit idle. Anyone in a business should be able to put it to use — not just the people who happen to know how to code or run a query.